This article reviews existing work and future opportunities in neuroevolution, an area of machine learning
in which evolutionary optimization methods such as genetic algorithms are used to construct neural
networks to achieve desired behavior. The article takes a neuroscience perspective, identifying where
neuroevolution can lead to insights about the structure, function, and developmental and evolutionary
origins of biological neural circuitry that can be studied in further neuroscience experiments. It proposes
optimization under environmental constraints as a unifying theme and suggests the evolution of
language as a grand challenge whose time may have come.
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